library(tidyverse)
## ── Attaching packages ───────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(patchwork)

load weather data

weather_df = 
  rnoaa::meteo_pull_monitors(
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "TMIN", "TMAX"), 
    date_min = "2017-01-01",
    date_max = "2017-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())
## Registered S3 method overwritten by 'hoardr':
##   method           from
##   print.cache_info httr
## using cached file: /Users/tiffanyfeng/Library/Caches/R/noaa_ghcnd/USW00094728.dly
## date created (size, mb): 2020-10-09 01:33:52 (7.525)
## file min/max dates: 1869-01-01 / 2020-10-31
## using cached file: /Users/tiffanyfeng/Library/Caches/R/noaa_ghcnd/USC00519397.dly
## date created (size, mb): 2020-10-09 01:27:50 (1.699)
## file min/max dates: 1965-01-01 / 2020-03-31
## using cached file: /Users/tiffanyfeng/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly
## date created (size, mb): 2020-10-09 01:29:08 (0.88)
## file min/max dates: 1999-09-01 / 2020-10-31
weather_df
## # A tibble: 1,095 x 6
##    name           id          date        prcp  tmax  tmin
##    <chr>          <chr>       <date>     <dbl> <dbl> <dbl>
##  1 CentralPark_NY USW00094728 2017-01-01     0   8.9   4.4
##  2 CentralPark_NY USW00094728 2017-01-02    53   5     2.8
##  3 CentralPark_NY USW00094728 2017-01-03   147   6.1   3.9
##  4 CentralPark_NY USW00094728 2017-01-04     0  11.1   1.1
##  5 CentralPark_NY USW00094728 2017-01-05     0   1.1  -2.7
##  6 CentralPark_NY USW00094728 2017-01-06    13   0.6  -3.8
##  7 CentralPark_NY USW00094728 2017-01-07    81  -3.2  -6.6
##  8 CentralPark_NY USW00094728 2017-01-08     0  -3.8  -8.8
##  9 CentralPark_NY USW00094728 2017-01-09     0  -4.9  -9.9
## 10 CentralPark_NY USW00094728 2017-01-10     0   7.8  -6  
## # … with 1,085 more rows
weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5)
## Warning: Removed 15 rows containing missing values (geom_point).

labels

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  )
## Warning: Removed 15 rows containing missing values (geom_point).

Scales

start with the same plot

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  ) +
  scale_x_continuous(
    breaks = c(-15, 0, 15),
    labels = c('-15 C', '0', '15 C')
  ) +
  scale_y_continuous(
    position = 'right'
  )
## Warning: Removed 15 rows containing missing values (geom_point).

color scales

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  ) + 
  scale_color_hue(name = "Location", h = c(100, 300))
## Warning: Removed 15 rows containing missing values (geom_point).

ggp_temp_plot = 
  weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  ) + 
  viridis::scale_color_viridis(
    name = "Location", 
    discrete = TRUE
  )

ggp_temp_plot
## Warning: Removed 15 rows containing missing values (geom_point).

Themes

shift the legend

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  ) + 
  viridis::scale_color_viridis(
    name = "Location", 
    discrete = TRUE
  ) +
  theme(legend.position = 'bottom')
## Warning: Removed 15 rows containing missing values (geom_point).

Change the overall theme

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name), alpha = .5) + 
  labs(
    title = "Temperature plot",
    x = "Minimum daily temperature (C)",
    y = "Maxiumum daily temperature (C)",
    caption = "Data from the rnoaa package"
  ) + 
  viridis::scale_color_viridis(
    name = "Location", 
    discrete = TRUE
  ) +
  theme_minimal()
## Warning: Removed 15 rows containing missing values (geom_point).

ggp_temp_plot + 
  ggthemes::theme_excel() + 
  theme(legend.position = "bottom")
## Warning: Removed 15 rows containing missing values (geom_point).

## Setting options

library(tidyverse)

knitr::opts_chunk$set(
  fig.width = 6,
  fig.asp = .6,
  out.width = "90%"
)

theme_set(theme_minimal() + theme(legend.position = "bottom"))

options(
  ggplot2.continuous.colour = "viridis",
  ggplot2.continuous.fill = "viridis"
)

scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d

Data argument in geom

central_park = 
  weather_df %>% 
  filter(name == "CentralPark_NY")

waikiki = 
  weather_df %>% 
  filter(name == "Waikiki_HA")

ggplot(data = waikiki, aes(x = date, y = tmax, color = name)) + 
  geom_point() + 
  geom_line(data = central_park)
## Warning: Removed 3 rows containing missing values (geom_point).

patchwork

faceting

weather_df %>% 
  ggplot(aes(tmin, fill = name)) +
  geom_density(alpha = .5) +
  facet_grid(. ~ name)
## Warning: Removed 15 rows containing non-finite values (stat_density).

multipanel plots

tmax_tmin_p = 
  weather_df %>% 
  ggplot(aes(x = tmax, y = tmin, color = name)) + 
  geom_point(alpha = .5) +
  theme(legend.position = "none")

prcp_dens_p = 
  weather_df %>% 
  filter(prcp > 0) %>% 
  ggplot(aes(x = prcp, fill = name)) + 
  geom_density(alpha = .5) + 
  theme(legend.position = "none")

tmax_date_p = 
  weather_df %>% 
  ggplot(aes(x = date, y = tmax, color = name)) + 
  geom_point(alpha = .5) +
  geom_smooth(se = FALSE) + 
  theme(legend.position = "bottom")

(tmax_tmin_p + prcp_dens_p) / tmax_date_p
## Warning: Removed 15 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).

Data Manipulation

weather_df %>%
  ggplot(aes(x = name, y = tmax, fill = name)) + 
  geom_violin(alpha = .5)
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

Control your factors

weather_df %>%
  mutate(
    name = factor(name),
    name = forcats::fct_relevel(name, c("Waikiki_HA", "CentralPark_NY", "Waterhole_WA"))
  ) %>% 
  ggplot(aes(x = name, y = tmax, fill = name)) + 
  geom_violin(alpha = .5)
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

densities for tmin and tmax simultaneously

weather_df %>% 
  select(name, tmax, tmin) %>% 
  pivot_longer(
    tmax:tmin,
    names_to = 'observation',
    values_to = 'temp'
  ) %>% 
  ggplot(aes(x = temp, fill = observation)) +
  geom_density(alpha = .5) + 
  facet_grid(~name) + 
  viridis::scale_fill_viridis(discrete = TRUE)
## Warning: Removed 18 rows containing non-finite values (stat_density).